Neuro-Oscillatory Function and Network Communication in ASD and Unaffected Siblings

2023

Using high-density electroencephalographic recordings of human brain activity, this program of research aims to investigate local and network neuro-oscillatory function in children with autism. We will record EEG data under a range of conditions (e.g., during motor activity, sensory stimulation, cognitive task performance, social cognition, and rest) and address specificity of dysfunctional local and network neuro-oscillatory processes. We will assess the relative strength of potential biomarkers with machine learning and graph theory based network analysis to determine the most informative combination of test conditions and EEG features to predict diagnosis and to predict dimensional scores on autism relevant traits/behaviors as measured by standardized dimensional scales. We will include an unaffected ASD-sibling cohort to address whether observed differences in oscillatory measures of neural information processing are likely related to heritable mechanisms underlying the development of autism, or if, alternatively, they are more likely the consequence of frank disease expression. Although this work will be performed in higher functioning children, we will emphasize approaches that are translatable to infants, more severe (e.g., syndromic) forms of autism, and animal models.

Performing Research or Evaluation

Health-Related Activities

Not Applicable

Racial or Ethnic Minorities

Single-County, Mulit-County, State

N/A